In recent years, StyleGAN and its multiple followups [1]}, [2]}, [3]}, [4]} have established themselves as the state-of-the-art unconditional image generators, owing to their ability to synthesize high resolution images of unprecedented quality.
The StyleGAN generator consists of two main components. First, a mapping network which converts a latent code \(z\) , sampled from a Gaussian distribution, into a vector \(w\)  in a learned latent space \(\mathcal {W}\) . These latent vectors are then fed into the second component - the synthesis network to control feature (or, equivalently, convolutional kernel) statistics across the different network layers. By traversing this intermediate latent space, \(\mathcal {W}\) , or by mixing different \(w\)  codes across different network layers, prior work demonstrated fine-grained control over semantic properties in generated images [5]}, [6]}, [7]}, [8]}. However, such latent-space traversal is typically limited to in-domain modifications. That is, it is constrained to the manifold of images with properties that match the initial training set. In contrast, here we aim to shift the generator between domains, moving beyond latent-space editing and towards semantically-aware fine-tuning.
